Swarm robots have sparked remarkable developments across a range of fields. While it is necessary for various applications in swarm robots, a fast and robust coordinate initialization in vision-based drone swarms remains elusive. To this end, our paper proposes a complete system to recover a swarm's initial relative pose on platforms with size, weight, and power (SWaP) constraints. To overcome limited coverage of field-of-view (FoV), the drones rotate in place to obtain observations. To tackle the anonymous measurements, we formulate a non-convex rotation estimation problem and transform it into a semi-definite programming (SDP) problem, which can steadily obtain global optimal values. Then we utilize the Hungarian algorithm to recover relative translation and correspondences between observations and drone identities. To safely acquire complete observations, we actively search for positions and generate feasible trajectories to avoid collisions. To validate the practicability of our system, we conduct experiments on a vision-based drone swarm with only stereo cameras and inertial measurement units (IMUs) as sensors. The results demonstrate that the system can robustly get accurate relative poses in real time with limited onboard computation resources. The source code is released.

翻译：集群机器人在多个领域引发了显著发展。尽管在集群机器人的各类应用中至关重要，但基于视觉的无人机集群在快速鲁棒的坐标初始化方面仍面临挑战。为此，本文提出了一套完整系统，可在尺寸、重量和功耗受限的平台上恢复集群的初始相对位姿。为克服视场有限覆盖的问题，无人机通过原地旋转获取观测数据；针对匿名测量问题，我们构建了一个非凸旋转估计问题，并将其转化为半定规划问题以稳定获取全局最优值，继而利用匈牙利算法恢复观测与无人机身份之间的相对平移及对应关系。为安全获取完整观测数据，我们主动搜索位置并生成可行轨迹以避免碰撞。为验证系统实用性，我们在仅配备立体相机和惯性测量单元的视觉无人机集群上开展实验。结果表明，该系统能在有限机载计算资源下实时鲁棒地获取精确相对位姿。相关源代码已开源。